A Study on the Effects of Earth Surface and Metrological ... · temperature, solar radiation, wind speed and relative humidity. This software requires at least temperature and precipitation

International Journal of Constructive Research in Civil Engineering (IJCRCE)

Volume 3, Issue 4, 2017, PP 99-120

ISSN 2454-8693 (Online)

DOI: http://dx.doi.org/10.20431/2454-8693.0304010

www.arcjournals.org

International Journal of Constructive Research in Civil Engineering (IJCRCE) Page | 99

A Study on the Effects of Earth Surface and Metrological

Parameters on River Discharge Modeling Using SWAT Model,

Case Study: Kasillian Basin, Mazandaran Province, Iran

Mohsen Ghane1, Sayed Reza Alvankar

1, Saeid Eslamian

2, Kaveh Ostad-Ali-Askari

3*,

Amir Gandomkar4

, Shahide Dehghan4

, Vijay P. Singh5, Nicolas R. Dalezios

6

1Civil Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran

2Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran

3*Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

4Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad, Iran

5Department of Biological and Agricultural Engineering &Zachry Department of Civil Engineering, Texas A

and M University, 321 Scoates Hall, 2117 TAMU, College Station, Texas 77843-2117, U.S.A. 6Laboratory of Hydrology, Department of Civil Engineering, University of Thessaly, Volos, Greece &

Department of Natural Resources Development and Agricultural Engineering, Agricultural University of

Athens, Athens, Greece.

1. INTRODUCTION

In order to construct dams, the monthly and annual inflow of rivers is an important factor in

determination of the dam reservoir volume. The input water flow can be calculated by the hydrometric

stations. In some regions with the lack of hydrometric stations, numerical models such as SWAT can

be used in order to estimate the runoff flowing into the dam. These models perform complicated

and very accurate calculations in a very short time.

2. MATERIALS

The study area is located in the northern forests of Alborz, which includes the villages of Sangdeh,

Darzikol, Soutkola, Valik Chal and Valik Ben. Kasilian basin is itself one of the sub-basins of Haraz

basin and is the second basin that has been equipped by the Ministry of Energy in Iran. The area of

Kasillian basin is about 66.81 square kilometers and the length of the main river is 16.8 kilometers.

The basin of this river is located at the geographic coordinates of 36°-02´ to 36°-11´ of latitude and

53°-10´ to 53°-26´ of longitude. The gradients being used in this study in percent include -2, 1, 2-5, 8-

12, 12-20, 30-60 and >60.

There is a hydrometric station at Valikben on the Kasillian River that has been established since 1970

with a longitude of 53°-17´ and a latitude of 36°-10´, which measures the river discharge. Figure 1

shows the location of the Kasillian basin.

Abstract: In order to design and construct many of the hydraulic structures such as dams, there is a need to

determine the amount of river discharge. In rivers without gauging stations, it is necessary to calculate their

inflows by using the hydrological models. SWAT is one of the numerical models that are widely used for this

purpose.

For calculating the basin discharge, the model requires effective metrological data such as precipitation,

temperature, wind speed, solar radiation and relative humidity on the one hand, and the data related to the

basin surface such as curve number and roughness coefficient on the other hand.

In this research, Kasillian River discharge has been calculated as a case study and has been verified using

data from Valikben hydrometric station, which is placed at the basin outlet. Furthermore, the impact of each

input data on the calculation of water flow has also been studied.

Keywords: SWAT, River Discharge, Precipitation, Solar Radiation, Wind Speed, Temperature

*Corresponding Author: Dr. Kaveh Ostad-Ali-Askari, Department of Civil Engineering, Isfahan (Khorasgan)

Branch, Islamic Azad University, Isfahan, Iran. Emails: [email protected], [email protected]

mailto:[email protected]

A Study on the Effects of Earth Surface and Metrological Parameters on River Discharge Modeling Using

SWAT Model, Case Study: Kasillian Basin, Mazandaran Province, Iran


Figure1. Position of Kasillian Basin

Figure2. Digital Elevation Model for Kasilian River Basin of Savadkooh Region ( Dem)

Figure3. Land Use Map for Kasilian River Basin of Savadkooh Region ( Land Use)




Figure4. Map of Soil for Kasilian River Basin of Savadkooh Region (Soil)

Figure5. End of zoning the region under consideration

In this model, the data including precipitation, temperature, solar radiation, wind speed, and relative

humidity has been used in a statistical period of February 1978 to February 1989 to simulate the

runoff. The mentioned statistical parameters of the Pole Sefid synoptic station data and Sangdeh and

Darzikola climatology stations, Valik Chal rain gauge station and Valikben hydrometric station have

been used.

2.1. An Introduction To SWAT Model

SWAT model was developed by The Agricultural Research Service (ARS), The US Department of

Agriculture, (Grassland Soil and Water Research Laboratory) in Texas. This model simulates the river

discharge. In order to estimate the river discharge, it is in need of climatic data such as precipitation,

temperature, solar radiation, wind speed and relative humidity. This software requires at least

temperature and precipitation data. The rest of the data can be simulated by the software. The

necessary maps in the software include the soil, land use and Digital Elevation Model (DEM) maps.

The SWAT model is performed in Arc GIS software.

Among the existing hydrologic models, the SWAT model is the most comprehensive model in

simulation of the ruling processes on the basin surface. SWAT is a conceptual-distributive model,

which has been initially designed for the large basins and has gradually been expanded for different

purposes. The system of SWAT model has three main parameters including model inputs, model

outputs and model main program.




The SWAT model includes 9 main parameters and about 22 secondary parameters, which simulates

the following six hydrologic and biologic phenomena (Arnold et al, 1990).

1. Daily stream flow [discharge]

2. Daily sediment yield

3. [Monthly and Yearly] Water balance

4. Water [quality] pollution

5. Producing the agricultural product

6. Estimation of the production of pasture plant coverage through application of management of cattle

grazing systems (Gholami, 1998)

For modeling objectives, one basin might be divided into many sub-basins. By dividing one basin into

many sub-basins, SWAT model simulates the place details. In this model, each basin is divided into

many sub-basins and each of the sub-basins into many Hydrologic Reaction Units (HRU), which are

homogenous from the land use viewpoint and the soil features.

For measuring the monthly discharge in the SWAT method, there is a need to the measured monthly

quantities in Synoptic, Climatology and rain gauge and hydrometric stations (such as daily rain,

minimum and maximum of daily temperature, sun ray, wind speed, and relative humidity which in

these cases in SWAT it is read in dbf).

2.2. Model Implementation Method

The SWAT software that specifies the river zoning and direction of the rivers and river discharge

simulation has been used for the concerned project. In SWAT, there is a need to topography map and

DEM map, the map of land use, the soil map and soil data for the concerned region and the data of

Synoptic, Climatology and Rain gauges’ stations and provision of look up table. (The necessary maps

are for GIS and SWAT software and the smaller scale maps would be better. In addition, better results

will be achieved for a larger number of years of data collection in Synoptic, Climatology and Rain

gauges’ stations).

All the three maps should have the same scale and unit so that in the SWAT software they could

overlap. The metrological data should be stored in DATABASE of SWAT software (SWAT 2009.

mdb). (It can be inserted into SWAT program) and data could be such as USERSOIL, WGN and … in

SWAT 2009. mdb.

The followings steps should be taken:

1. Determination of the method of input data preparation into the model;

2. Modeling stages in ARCSWAT;

3. Model performance, data storage and output data.

The files of Arcsw at_documentation in Arcsw at help describe the performance methods and the files

of swat-input-output.pdf and theoretical swat 2009 has been used.

The files should be in Excel form and to be inserted into part of SWAT database (swat2009.mdb).

Then for the application in SWAT model, they should be used in form of dbf and txt files. The ending

and beginning years of all input data into SWAT model should be equal. The quantity of rain data is

in millimeter and it can be inserted in form of less than daily, monthly and annually. The data of Abali

Synoptic Station has been used instead of Pole-Sefid Synoptic station, whose data is similar to the

area under consideration.

These images have been cut due to the high capacity of DEM main files and land use and soil data so

that they could better show the results in SWAT [1-24].

The cell-size of the map was considered as 50 in order to have all three maps in agreement with each

other.

The type of land use and their numbers in the map should be noted down for connection to the

SWAT. We considered two types of land use in the concerned basin including:




1. FRSD

2. FRSE

In addition, the type of soil and their numbers in the map should be noted down for connection to the

SWAT.

Soil data should be taken by Soil Lab in the region and with regard to the type of soil in the map to be

connected to it. Then, the soil data from the Soil Lab should be connected to the type of soil in the

map. As soil data was not available, the world map of soil data was used.

The data related to Synoptic station was inserted. Because of the similarity in weather conditions in

Abali and Pole-Sefid Synoptic Stations, and due to the fact that the Pole-Sefid data was not available,

so Abali data was inserted for Pole-Sefid.

In addition, the daily rain data of two Climatology stations and two Rain gauge stations and the daily

maximum and minimum temperature of SWAT of the two climatology stations (with the conversion

into dbf or txt files) from 1979 to 1989 (11 years) was inserted and finally, the latitude and longitude

of the concerned locations was inserted.

Then, the soil and land use and Synoptic map data should be connected to the map.

Using SWAT 2009 INPUT-OUTPUT file, land use data was connected to SWAT. Then, Synoptic

data was inserted (it should be in agreement with the geographic legends of the land use, soil and

DEM maps). The concerned files should be converted into dbf for conversion into SWAT.

For drainage gradient of the basin, the SCS method was used which divides the gradients into 5

classes (Table 4).

Table1. Sample of Soil General Data

TEX

TUR

E

SOL_

CRK

ANION_E

XCEL

SOIL_Z

MX

HYD

GRP

NLAY

ERS

CMPP

CT

S5I

D

SN

AM

SE

QN

MU

ID

OBJE

CTID

LOA

M

0.5 0.5 1000 C 2 Soil

_1

203

LOA

M

0.5 0.5 1000 C 2 Soil

_2

204

Table2. Sample of Data related to each of the Soil Horizons

SOL_

EC1 USLE

_K1 SOL_A

LB1 ROC

K1 SAN

D1 SIL

T1 CLA

Y1 SOL_C

BN1 SOL_

K1 SOL_A

WC1 SOL_

BD1 SO

L_

Z1 0 0.3108 0.0184 0 30 49 21 1.7 9.23 0.175 1.3 30

0 0 0.3108 0.0184 0 30 49 21 1.7 9.23 0.175 1.3 30

0

The data which are surely needed includes the followings:

Number of layers= ( NLAYERS)

Soil hydrologic groups (using infiltration and use) =HYDGRP

Soil depth related to all existing horizons( millimeter) ( SOL_ZM)

Soil Texture=Texture

Note: The following data should be provided for all existing horizons in the basin soil units:

Thickness of Horizon 1 in millimeter=SOL_Z1

Soil Volume Density in Horizon 1( gram per cubic centimeter)

Water Accessible Capacity in Horizon 1 ( mm) =SOL_AWC1

Saturation Electrical Conductivity in Horizon 1 (mm per hour) =SOL_K1

Organic Materials in Horizon 1 ( %) =SOL_CBN1




Clay in Horizon 1(%) : CLAY 1

Silt in Horizon 1(%) =SILT1

SAND1=Sand in Horizon 1(%)

Table3. Data related to Sputnik station and land use and soil

ELEVATION LONG LAT NAME ID

1350 53.10 36.05 POLSEFID 1

VALUE LANDUSE LANDUSE

19 mix(bagh_lowforest) FRSD

24 denseforest FRSE

VALUE NAME

13 SOIL_1

24 SOIL_2

Table4. Gradient classification in SCS suggested method

Gradient in percent Gradient class

5-0 1

10-5 2

20-10 3

40-20 4

>40 5

Soil and land use data and gradient in HRU should be in agreement with each other. The data related

to Synoptic station, temperature and rain was inserted in the write input table in the weather station

section. Rain and temperature data are inserted on a daily basis. In the sub-basin data part, all the

changes related to sub-basins can be applied and other changes related to SWAT can be applied in

SWAT input edit part. In the last part, SWAT simulation can be performed upon the software data

being completed and saved. All the saved data can be found in Table shoot at the SWAT output

section.

2.3. The Effect of Soil Type

In this research, the optimum curve number and over land roughness coefficient of the basin surface

has been studied. Out of the required metrological parameters, only precipitation data has been used

first to obtain the optimum curve number and roughness coefficient of the flow in the basin. In the

beginning, SWAT model was ran considering a curve number of CN2= 67 and an over land roughness

coefficient of OV_N=0.1. The results can be seen in Figure 6.

Figure6. Simulated average discharge of SWAT model and average discharge being observed in Kasillian basin

In order to optimize the parameters, different curve numbers (CN) and over land roughness

coefficients have been used. The discharge equations using these parameters are shown in Tables 5

and 6, and Figs. 7 to 10. In a comparison to the recorded discharges in the gauging station and the

calculated discharge, an optimum curve number of 67 and an over land roughness coefficient of 0.1

0

0.5

1

1.5

2

2.5

1 5 9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

10

1

10

5

Dis

char

ge

(Cm

s)

MonthDischarge (Cms) Discharge observes (Cms)




was obtained for the basin. Then, considering the two mentioned values, other input parameters of

SWAT model in river discharge simulations were studied. The effect of metrological parameters on

river discharge simulation in SWAT model was investigated separately or together and the results

were compared to the observed discharges. It is worth noting that at this stage of calculations, only the

precipitation data has been used as the model input data [25-56].

Figure7. Changes of curve number as compared with the simulated discharge

Figure8. Changes of roughness coefficient as compared with the simulated discharge

Figure9. River discharge average from different curve number as compared with the observed discharge

0

0.5

1

1.5

2

2.5

3

1 6

11

16

21

26

31

36

41

46

51

56

61

66

71

76

81

86

91

96

10

1

10

6

Dis

char

ge(C

ms)

Month

cn= 67 observed cn= 69 cn= 72 cn= 76




Figure10. River discharge average with different basin over land roughness coefficient as compared with the

observed discharge

Table5. The study of the impact of curve number on the calculated discharge average

Curve Number 67 69 72 76

Simulated

discharge average

(m3/s)

0.475787 0.477227 0.38084 0.388203

Observed discharge

average (m3/s)

0.4989532 0.4989532 0.4989532 0.4989532

Calculations errors

(m3/s)

0.123166 0.121726 0.118113 0.11075

Table6. The study of the impact of roughness coefficient on the calculated discharge average

Basin Over land

roughness

coefficient

0.05 0.1 0.15 0.2

Simulated

discharge average

(m3/s)

0.375787 0.375787 0.375784 0.375774

Observed

discharge average

(m3/s)

0.498953 0.498953 0.498953 0.498953

Difference

between the

observed and

simulated

discharge average

(cubic meter per

second)

0.123166 0.123166 0.123169 0.123179

2.4. Running the Model Considering Other Metrological Parameters

At this stage, in addition to precipitation data, other metrological parameters including temperature,

relative humidity, wind speed and solar radiations have been entered to SWAT model and an average

discharge of 0.5704 m3/s has been obtained, as can be seen in the first row of Table 7. As can be seen

from the Table 7, the recorded discharge in the gauging station in the modeling period (February 1998

to February 1989) has been 0.4989 m3/s and the calculation error was 0.0714 m

3/s corresponding to

14.32%.

Table7. The observed and simulated monthly discharge average changes in the application of SWAT model with

each of the input data

Row Input data Simulated

discharge

Observed

discharge

Difference of

observed and

Error

Percentage

0

0.5

1

1.5

2

2.5

3

1 5 9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

10

1

10

5

10

9

Dis

char

ge(C

ms)

monthn= .05 n=.01 n=.2 n= 0.1 observed




average (cubic

meter per

second)

average (cubic

meter per

second)

simulated

discharge

average

(Cubic meter

per second)

1 (Prc., Temp.,Rad.,Win.,RH) 0.5704225 0.498953704 0.071468796 14.32

2 (Pre. , Temp, Win, RH) 0.588334 0.498953704 0.089271 17.89

3 (Pre. Temp, Rad., Win) 0.293554 0.498953704 -0.205399 41.16

4 (Pre., Temp.,RH, Rad) 0.569122407 0.498953704 0.070168703 14.06

5 (Pre., Rad., Win., RH) 0.722399 0.498953704 0.447829 89.75

6 (Temp, Rad, Win, RH) 0.086537 0.498953704 -0.412416 82.65

7 ( Temp, Rad, RH) 0.090831 0.498953704 -0.182043 36.48

8 (Temp, Rad, Win) 0.0254080 0.498953704 -0.473545 94.9

9 (RH, Rad, Win) 0.149367 0.498953704 -0.349586 70.6

10 (Prc., Temp, Win.,) 0.298773889 0.498953704 -0.200179815 40

11 (Pre., Temp., RH) 0.582392315 0.498953704 0.083438611 16.72

12 (Prc, Temp, Rad) 0.296394444 0.498953704 -0.20255926 41

13 (Prc, Temp) 0.300483333 0.498953704 -0.198470371 39

14 (Prc. , RH) 0.666655 0.498953704 0.167702 33.61

15 (Rad, RH) 0.154092 0.498953704 -0.344861 69.12

16 (Rad.,Win) 0.04151318 0.498953704 -0.457439 91.68

17 (Pre.) 0.388740926 0.498953704 -0.110212778 22.08

18 (Temp.) 0.121320904 0.498953704 -0.3776328 75.68

19 (Rad.) 0.044181033 0.498953704 -0.4547719 91.14

20 (Win) 0.251754 0.498953704 -0.247199 49.54

21 (RH) 0.482683 0.498953704 -0.01627 3.26

In order to study the effects of all the necessary metrological parameters on the calculated discharge in

SWAT model, every input parameter to the model was removed. Model results are presented in other

rows of Table 7. For example, row 2 of Table 7 shows that when the solar radiation data was not

inserted into the model, the average calculated discharge was 0.588 and the calculation error increased

to 17.89 percent. The third row of this table shows that when the relative humidity data was not

inserted, the average of mean discharge of the period decreased and the calculation error was 41.16

percent. The fifth row shows that when the temperature data was not inserted to the model, discharge

significantly increased and the error was 89.75 percent compared with the calculated value [57-98].

The sixth row shows that when the precipitation data was not inserted, the average of mean discharge

of the period decreased and the error was 82.65 percent. The seventh row shows that when the

precipitation and wind speed data was not inserted to the model, the average of mean discharge of the

period decreased and the error was 36.48 percent.

The eighth row shows that when the precipitation and relative humidity data was not inserted, the

average of mean discharge of the period decreased and the error was 94.4 percent. The ninth row

shows that when the precipitation and temperature data was not inserted, the average of mean

discharge of the period decreased and the error was 70.06 percent. In the eleventh row, by omission of

the solar radiation and wind speed data, the average calculated discharge showed an error of 16.72

percent [99-115].

In the twelfth row, by omission of the relative humidity and wind speed data, the average of mean

discharge decreased and the error was being 41 percent.

The thirteenth row shows that by inserting the precipitation and temperature data, the average of mean

discharge of the period decreased and the error was 39 percent.

In the fourteenth row, by omission of the temperature, solar radiation and wind speed data, the

average of mean discharge of the period increased and the error was 33.61 percent.

In the fifteenth row, by the omission of the temperature, relative humidity and wind speed data, the

average of mean discharge of the period decreased and the error was 13.28 percent.

In the eighteenth row, by inserting the temperature and solar radiation input data, the average of mean

discharge of the period decreased with an error of 94.8 percent.

In the twentieth row, by inserting the temperature and solar radiation input data, the average of mean

discharge of the period decreased with an error of 4.66 percent.




In the twenty-third row, by inserting the precipitation data, the average of mean discharge decreased

with an error of 22.08 percent.

In the twenty-fourth row, by inserting the temperature data, the average of mean discharge of the

period decreased with an error of 75.68 percent.

In the twenty-seventh row, by inserting the relative humidity input data, the average of mean

discharge of the period decreased with an error of 3.26 percent. The negative and positive values of

the column before the last column show the decreased and increased simulated values of the discharge

in comparison to the observed discharge values, respectively.

In Figure 11, the values of average monthly discharge that were calculated at eight different

conditions of Table 7 are compared with the recorded discharge values in the gauging station. Figs. 12

and 13 show the plot of the calculated and recorded discharge values in the first to seventeenth rows

of Table 7 [116-123].

3. RESULTS

1. With an increase of 13.43 percent in the curve number, the simulated value of the average

monthly discharge got 2.52 percent closer to the observed average discharge value.

2. With an increase of 0.15 in the over land roughness coefficient of the basin, the simulated

discharge got 0.01 percent closer to the observed discharge.

3. According to Figs. 12 and 13, the precipitation, temperature and relative humidity data have a

greater impact on the calculated discharges compared with the solar radiation and wind speed

data.

4. Using all the metrological parameters, the average of mean discharge of the period increased with

an error of 14.32 percent.

5. Figs. 12 and 13 show the effect of inserting the relative humidity input data on the average of

mean discharge compared with other parameters (separately). By applying the (RH) and (Win,

RH) parameters, the simulated average discharge by the model got closer to the observed value.

6. Figs. 12 and 13 show the effect of inserting the temperature input data on a decrease in the

average of mean discharge.

7. With insertion of the temperature, rain, relative humidity, solar radiation and wind speed input

data, the average of the observed monthly discharge decreased by 16 percent in comparison to the

simulated average monthly discharge.

8. With insertion of the temperature and rain input data, the average of the observed monthly

discharge increased by 38 percent in comparison to the simulated average monthly discharge.

9. With insertion of the temperature, rain and wind speed input data, the average of the observed

monthly discharge increased by 40 percent in comparison to the simulated average monthly

discharge.

10. With insertion of the temperature, rain, relative humidity and solar radiation input data, the

average of the observed monthly discharge decreased by 14 percent in comparison to the

simulated average monthly discharge.

11. With insertion of the temperature, rain, and relative humidity input data, the average of the

observed monthly discharge decreased by 18 percent in comparison to the simulated average

monthly discharge.

12. With insertion of the temperature, rain and solar radiation input data, the average of the observed

monthly discharge increased by 40 percent in comparison to the simulated average monthly

discharge.

13. With insertion of the temperature input data, the average of the observed monthly discharge

increased by 75 percent in comparison to the simulated average monthly discharge.

14. With insertion of the rain input data, the average of the observed monthly discharge increased by

22 percent in comparison to the simulated average monthly discharge.

15. SWAT software presented an almost suitable results in the estimation of the average monthly

discharge with regard to the input data of rain, temperature and other necessary data.




4. DISCUSSION

In 2012, Bastani Allah Abadi evaluated the SWAT2009 model in Kordan River and achieved

acceptable results by river simulation and understanding the basin using SWAT model.

In 2003, Gholami used the SWAT model to simulate the monthly average discharge of Emameh

basin, one of the Sub-basins of Jajerood River. The results showed that the model has a high

sensitivity to the roughness of the land surface.

In 1933, Babaei and Sohrabi evaluated the SWAT model in calculation of Zayandehrood basin flow

(1), (6). Omani et al. (1931) used the SWAT model for

simulation of the river flow in Qareh Sar sub-basin in the North-West of Karkheh River and showed a

higher sensitivity analysis for the curve number parameter [1-7].

In 2005, Lin et al. calibrated the SWAT model for Atrova river basin in Kaneon with an area of 1680

square kilometers. The calibration results of the daily and monthly flows were satisfactory. The

results showed that the model has a good performance in flow prediction.

In 1996, Binger simulated the runoff rate by the SWAT model in a basin in the north of Mississippi

for 10 years. The SWAT model indicated acceptable results in simulation of daily and yearly runoff

from some sub-basins with the exception of one sub-basin full of affluent trees.

In 2002, Khalil Ahmad et al. employed SWAT model in a research site near Nashra, which the results

showed that the model has a high potential to forecast the undersurface flows under different climatic

conditions, slant and soil.

In 2003, Van Lou and Garberch estimated the runoff under change of weather conditions for three

sub-basins in experimental watershed of Little Vashina River in the south-west of Oklahoma by

SWAT model. The results showed that SWAT can forecast the rate of runoff in dry, mean and humid

conditions sufficiently.

In 2016, Ye Tuo et al. tested the same model. The outcome of the study cleared that precipitation is

the main source of uncertainly and different precipitation datasets in SWAT models lead to the best

different estimable ranges for the calibrated parameters.

In 2016, Sun Sook et al. used SWAT in Haean highland agricultural catchment (62.8 Km2) which was

the best area to test uplands above 600 m of elevation and it happened by using SWAT (soil and

Water Assessment Tool). The result showed the RSM BMP indicating the sediment reduction of 3.0%

for 6.0% runoff reduction up to 14.1% for 17.0% runoff reduction and T-P reduction of 1.3% for

6.0% runoff reduction up to 6.8% for 17% runoff reduction along with negative effect of total

nitrogen (T-N) up to -3.7% for 12% runoff reduction.

The main focus of Bingqing Lin et al. (2015) using SWAT was on the Runoff responses to land use

change with daily measurement. Jinjiang, as a natural area for collecting rainwater in southeast China

with a humid sub-tropical climate, was used for simulations. Three stations lead to the reproduction of

annual, monthly and daily runoff processes over nine years (2002-2010) and the result was satisfying.

Simly Dam watershed in Saon basin in the north-east of Islamabad was another place SWAT was

applied for the Hydrology by Shimaa, M. Ghoraba in 2015. The model was calibrated from 1990 to

2001 and evaluated from 2002 to 2011. The aim of whole process was to simulate the stream flow.

The evaluation resulted in a good performance for both calibration and validation periods between

measured and simulated values of both annual and monthly scale discharge.

The Xedone River basin in an area of 7,224,61 km2 in the southern part of Laos was evaluated by

Bounhieng Vilaysane et al. who used SWAT model to predict stream flow in the river basin. The

model was tested for two periods of 1993-2000 and 2001-2008 and the result was satisfactory both in

monthly and daily phases of simulation and also in measured calibration and validation [124-193].

5. CONCLUSIONS

Application of SWAT model, its accuracy and scaling of Hydrological stream flow modeling in the

Kasillian River was prospering and the results of the time period between February 1978 and

February 1989 fulfills the accuracy and scaling expectancy. The SWAT modeling indicates signs of




success in simulation for a period of one month and it would be applicable in the basin while shows

capability of investigating climate and land changes. Moreover, projecting for probable dam

construction and flood in future and managing them is possible by this model. Such management will

not be separated from managing water resource in Kasillian River. Therefore, the SWAT model will

be effective in the sustainable development of the country.

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[20] Dehghan Sh, Kamaneh S.A.A., Eslamian S, Gandomkar A, Marani-Barzani M, Amoushahi-Khouzani M,

Singh V.P., Ostad-Ali-Askari K. 2017, Changes in Temperature and Precipitation with the Analysis of

Geomorphic Basin Chaos in Shiraz, Iran. International Journal of Constructive Research in Civil

Engineering (IJCRCE), 3(2): 50-57.

[21] Eslamian S, Mirabbasi-Najafabadi R, Ostad-Ali-Askari K. Advance Engineering Statistics (Simulation and

Modeling of Uncertainty and Sensitivity Analysis). Kankash Publisher. First Edition, 2017. ISBN: 978-60

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[23] Ostad-Ali-Askari K, Eslamian S, Shayannejad M, et al. Groundwater Hydrodynamic. Horoufchin

Publisher. First Edition, 2016. ISBN: 978-600-7419-53-3. Isfahan, Iran.

[24] Ostad-Ali-Askari K, Shayannejad M, Ghorbanizadeh-Kharazi H. 2017, Artificial Neural Network for

Modeling Nitrate Pollution of Groundwater in Marginal Area of Zayandeh-rood River, Isfahan, Iran.

KSCE Journal of Civil Engineering, 21(1):134-140. Korean Society of Civil Engineers. DOI 10.1007/ s1

2205-016-0572-8.

[25] Shayannejad M, Ostad-Ali-Askari K, Ramesh A, Singh V.P., Eslamian S. 2017, Wastewater and

Magnetized Wastewater Effects on Soil Erosion in Furrow Irrigation. International Journal of Research

Studies in Agricultural Sciences (IJRSAS), 3(8): 1-14. http://dx.doi.org/10.20431/2454-6224.0308001.

[26] Shayannejad M, Soltani-Toudeshki A.R, Arab M.A, Eslamian S, Amoushahi-Khouzani M, Marani-

Barzani M, Ostad-Ali-Askari K. 2017, A Simple Method for Land Grading Computations and its

Comparison with Genetic Algorithm (GA) Method. International Journal of Research Studies in

Agricultural Sciences (IJRSAS), 3(8): 26-38.

[27] Mohieyimen P, Eslamian S, Ostad-Ali-Askari K, Soltani M. 2017, Climate Variability: Integration of

Renewable Energy into Present and Future Energy Systems in Designing Residential Buildings.

International journal of Rural Development, Environment and Health Research(IJREH), 1(2): 18-30.

[28] Shayannejad M, Ostad-Ali-Askari K, Eslamian S, et al. 2017, Flow Hydraulic Investigation of the

Wastewater on the Soil and Magnetic Field Effects in This Field. International Journal of Constructive

Research in Civil Engineering (IJCRCE), 3(3): 1-15.

[29] Shayannejad M, Eslamian S, Singh V.P., Ostad-Ali-Askari K, et al. 2017, Evaluation of Groundwater

Quality for Industrial Using GIS in Mountainous Region of Isfahan Province, Koh-Payeh, Isfahan, Iran.


[30] Eslamian S, P. Singh V, Ostad-Ali-Askari K, R. Dalezios N, Yihdego Y, et al. 2017, Assessment of

Aridity Using Geographical Information System in Zayandeh-Roud Basin, Isfahan, Iran. International

Journal of Mining Science (IJMS), 3(2): 49-61.

[31] Askari Z, Samadi-Boroujeni H, Fattahi-Nafchi R, Yousefi N, Eslamian S, Ostad-Ali-Askari K, P. Singh V,

R. Dalezios N. 2017, Prediction Comparison of Flow Resistance in Channels with Rounded and Angular

Coarse Rough Beds. American Research Journal of Civil And Structural, 3(1): 1-15.

[32] Ghane M, Alvankar S.R., Eslamian S, Amoushahi-Khouzani M, Gandomkar A, Zamani E, Marani-Barzani

M, Kazemi M, Soltani M, Dehghan SH, P. Singh V, Ostad-Ali-Askari K, HaeriHamedani M, Shirvani-

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Parameters: A Case Study of Kasillian Watershed, Mazandaran, Iran. International Journal of Research

Studies in Agricultural Sciences (IJRSAS), 3(10): 1-20.

[33] Shayannejad M, Abedi M.S., Eslamian S, Ostad-Ali Askari K, Gandomkar A, Cheng A, et al. 2017, The

Contribution of Artificial Charging in Optimal Exploitation of Water Resources, Isfahan, Iran.

InternationalJournal of Mining Science (IJMS), 3(3): 9-20.

[34] Eslamian S, Ostad-Ali Askari K, et al. 2017, Guidelines to Optimal Design of Furrow Irrigation Based on

Plants, Soil and Furrow Specifications. International Journal of Constructive Research in Civil

Engineering (IJCRCE), 3(4): 20-39.

http://dx.doi.org/10.20431/2454-6224.0308001




[35] Galoie, M., Eslamian, S., and A. Motamedi, 2014, An Investigation of the Influence of a Retention Dam

on Flood Control In a Small Catchment Area in Austria, Journal of Flood Engineering, Vol. 5, No. 1/2, 1–

15.

[36] Hadizadeh, R., Eslamian, S. and Chinipardaz, R., 2013, Investigation of long-memory properties in

streamflow time series in Gamasiab River, Iran’, Int. J. Hydrology Science and Technology, Vol. 3, No. 4,

319–350.

[37] Amiri, M. J. and S. S. Eslamian, 2010, Investigation of climate change in Iran, Journal of Environmental

Science and Technology, Vol. 3, No. 4, 208-216.

[38] Eslamian, S. S., M. Naderi-beni, M. M. Kohansal, S. Pouriamehr, and A. Nasri, 2014, Investigation of

temperature and precipitation changes in Isfahan stations using parametric and nonparametric tests, The

4th International Conference on Environmental Challenges and Dendrochronologoy, Sari, Iran.

[39] Bazrkar, M. H., Zamani, N., Eslamian, S. S., 2014, Investigation of Landuse Impacts on Sediment Yield

using a SWAT (Case Study: Chamgodalan Reservoir Watershed, Iran), Proceeding of 3rd ScienceOne

International Conference on Environmental Sciences, UAE.

[40] Eslamian, S. S. and S. A. Gohari, 2006, Investigation of Flooding Process in South-Esfahan Basin,

International Congress of Islamic World Geographers, Esfahan University, Isfahan.

[41] Eslamian, S. S., Ghoudarzi, A. and R. Nazari, 2006, Investigation of the Changes of Permeability, Physical

and Chemical Characteristics of Sediment Basins for Artificial Recharge In Bagh-E-Sorkh Region,

Shahreza, Isfahan, 22nd Annual International Conference on Soils, Sediments and Water, University of

Massachusetts at Amherst, USA.

[42] Abedi-Koupai, J., Ghaheri, E., Eslamian, S.S. and Hosseini, H., 2013, Investigation the Kinetic Models of

Biological Removal of Petroleum Contaminated Soil around Oil Pipeline Using Ryegrass, Water and

Wastewater, Vol. 89, No. 1, 62-68.

[43] Ghasemi, A., Eslamian, S. S. and S. M. J. Nazemosadat, 2007, Impact of wind cooling on human

comfortability in various regions of Iran, Journal of Research and Investigation of Literature and

humanitarian Sciences Faculty, Vol. 24, No. 3, 13-26.

[44] Nasri, M., Najafi, A., Modarres, R. and S. S. Eslamian, 2007, Flood Regional Modelling in south-western

Ardestan watershed, Journal of Research and Investigation of Literature and Humanitarian Sciences

Faculty, Vol. 27, No. 6, 17-32.

[45] Godarzi, A, S. S. Eslamian and J. Abedi-Koupai, 2004, Investigation of Infiltration changes and physical

and chemical characteristics of sediments in Bagh-e-sorkh Shahreza flood spreading strips, Journal of

Research in Agricultural Sciences, Vol. 3, No. 1, 33-43.

[46] Nosrati, K., S. S. Eslamian and A. Shahbazi, 2004, Investigation of climate change effect on hydrologic

drought, Journal of Agriculture, Vol. 6, No. 1, 49-56.

[47] Yousefi, N., Khodashenas, S. R., Eslamian, S. and Askari, Z. 2016. Estimating width of the stable

channels using multivariable mathematical models, Arab. J. Geosci., Vol. 9, No. 321, DOI 10.1007 /s12 51

7-016-2322-0.

[48] Banihabib, M. E., Zahraei, A. and Eslamian, S., 2016. Dynamic Programming Model for the System of a

Non‐ Uniform Deficit Irrigation and a Reservoir. Irrigation and Drainage, Vol. 66, No. 1, 71–81

[49] Zalewski, M., McClain, M. and Eslamian, S., 2016. New challenges and dimensions of Ecohydrology–

enhancement of catchments sustainability potential, Ecohydrology and Hydrobiology, 16, 1–3

[50] Zalewski, M., McClain, M. and Eslamian, S., 2016. Ecohydrology–the background for the integrative

sustainability science, Ecohydrology and Hydrobiology, No. 16, 71–73.

[51] Kouhestani, S., Eslamian, S.S., Abedi-Koupai, J. and Besalatpour, A.A., 2016. Projection of climate

change impacts on precipitation using soft-computing techniques: A case study in Zayandeh-rud Basin,

Iran. Global and Planetary Change, No. 144, 158–170.

[52] Teimouri, A., Eslamian, S. and Shabankare, A. 2016. Removal of Heavy Metals from Aqueous Solution

by Red Alga Gracilaria Corticata as a New Biosorbent, Trends in Life Science, Vol. 5, No. 1, 236-243.

[53] Amiri, M.J., Hamidifar, H., Bahrami, M. and Eslamian, S. (2016) ‘Optimisation of deficit-irrigation under

variable seasonal rainfall and planning scenarios for rice in a semi-arid region of Iran’, International

Journal of Hydrology Science and Technology, Vol. 6, No. 4, 331–343.

[54] Salarijazi, M., Abdolhosseini, M., Ghorbani, K. and Eslamian, S. 2016, Evaluation of quasi-maximum

likelihood and smearing estimator to improve sediment rating curve estimation’, International Journal of

Hydrology Science and Technology, Vol. 6, No. 4, 359–370.

[55] Amiri, M.J., Bahrami, M., Hamidifar, H. and Eslamian, S., 2016. Modification of furrow Manning's

roughness coefficient estimation by finite difference technique under surge and continuous flow.

International Journal of Hydrology Science and Technology, Vol. 6, No. 3, 226-237.




[56] Zahraei, A., Eslamian, S. and Saadati, S., 2016. The effect of water extraction time from the river on the

performance of off-stream reservoirs. International Journal of Hydrology Science and Technology, 6(3):

254-265.

[57] Zareian, M. J. and Eslamian, S., 2016, Variation of water resources indices in a changing climate,

International Journal of Hydrology Science and Technology, Vol. 6, No. 2, 173 – 187.

[58] Fathian, F., Dehghan, Z.., Eslamian, S., Adamowski, J., 2016, Assessing Irrigation Network Performance

Based on Different Climate Change and Water Supply Scenarios: A Case Study in Northern Iran,

International Journal of Water, Accepted.

[59] Fathian, F., Dehghan, Z.., Eslamian, S., 2016, Evaluating the impact of changes in land cover and climate

variability on streamflow trends (case study: eastern subbasins of Lake Urmia, Iran), J. Hydrology


[60] Dalezios, N. R. and Eslamian, S, 2016, Regional design storm of Greece within the flood risk management

framework, Int. J. Hydrology Science and Technology, Vol. 6, No. 1, 82–102.

[61] Kamali, M. I., Nazari, R., Fridhosseini, A., Ansari, H., Eslamian, S., 2015, The Determination of

Reference Evapotranspiration for Spatial Distribution Mapping Using Geostatistics, Vol. 29: 3929–3940.

[62] Talchabhadel , R., Shakya, N. M. Dahal , V., and Eslamian, S., 2015, Rainfall Runoff Modelling for Flood

Forecasting (A Case Study on West Rapti Watershed), Journal of Flood Engineering, Vol. 6, No. 1, 53-61.

[63] Yousefi, N., Safaee, A., Eslamian, S., 2015, The Optimum Design of Flood Control System Using

Multivariate Decision Making Methods (Case Study: Kan River Catchment Basin, Iran), Journal of Flood

Engineering, Vol. 6, No. 1, 63-82.

[64] Banihabib, M. E., Zahraei, A. and Eslamian, S., 2015, An integrated optimization model of reservoir and

irrigation system applying uniform deficit irrigation, Int. J. Hydrology Science and Technology, Vol. 5,

No. 4, 372–385.

[65] Fathian, F., Prasad, A. D., Dehghan, Z.., Eslamian, S., 2015, Influence of land use/land cover change on

land surface temperature using RS and GIS techniques, Int. J. Hydrology Science and Technology, Vol. 5,

No. 3, 195–207.

[66] Abedi-koupai, J., Mollaei, R., Eslamian, S. S., 2015, The effect of pumice on reduction of cadmium uptake

by spinach irrigated with wastewater, Ecohydrology and Hydrobiology, Vol. 15, No. 4, 208-214.

[67] Kamali, M. I., Nazari, R., Faridhosseini, A., Ansari, H., Eslamian, S., 2015, The Determination of

Reference Evapotranspiration for Spatial Distribution Mapping Using Geostatistics, Water Resources

Management, 29:3929-3940.

[68] Valipour, M., Gholami Sefidkouhi, M. A., Eslamian, S., 2015, Surface irrigation simulation models: a

review, Int. J. Hydrology Science and Technology, Vol. 5, No. 1, 51-70.

[69] Esmailzadeh, M., Heidarpour, M., Eslamian, S. , 2015, Flow characteristics of sharp-crested side sluice

gate, ASCE's Journal of Irrigation and Drainage Engineering, Vol. 141, No. 7, 10.1061/(ASCE)IR.1943-

4774.0000852.

[70] Zareian, M. J., Eslamian, S. and Safavi, H. R., 2015, A modified regionalization weighting approach for

climate change impact assessment at watershed scale, Theor. Appl. Climatol., 122:497-516.

[71] Boucefiane A., Meddi M., Laborde J. P., Eslamian S. S., 2014, Rainfall Frequency Analysis Using

Extreme Values, Distributions In the Steppe Region of Western Algeria, Int. J. Hydrology Science and

Technology, Vol. 4, No. 4, 348-367.

[72] Valipour, M., Eslamian, S., 2014, Analysis of potential evapotranspiration using 11 modified temperature-

based models, Int. J. Hydrology Science and Technology, Vol. 4, No. 3, 192-207.

[73] Meddi, M., Toumi, S., Assani, A. A., Eslamian, S., 2014, Regionalization of Rainfall Erosivity in Northern

Algeria, Int. J. Hydrology Science and Technology, Vol. 4, No. 2, 155-175.

[74] Zohrabi, N., Massah Bavani, A., Goodarzi, E., S. Eslamian, 2014, Attribution of temperature and

precipitation changes to greenhouse gases in northwest Iran, Quaternary International, Vol. 345, 130-137.

[75] Farshad F., Dehghan, Z., Eslamian, S., H. Bazrkar, 2015, Trends in hydrologic and climatic variables

affected by four variations of Mann-Kendall approach in Urmia Lake basin, Iran, Hydrological Sciences

Journal, DOI:10.1080/02626667.2014.932911.

[76] Fazlolahi, H. and S. S. Eslamian, 2014, Using wetland plants in nutrient removal from municipal

wastewater, Int. J. Hydrology Science and Technology, Vol. 4, No. 1, 68–80.

[77] Farshad F., Dehghan, Z. and S. Eslamian, 2014, Analysis of Water Level Changes in Lake Urmia Based

on Data Characteristics and Nonparametric Test, Int. J. Hydrology Science and Technology, Vol. 4, No. 1,

18–38.

http://www.sciencedirect.com/science/journal/10406182

http://www.inderscience.com/ospeers/admin/editor/step.php?fromaccepted=yes&articleID=75838&jid=364&jeID=7442&editors=









[78] Varshney, L., Saket, R. K. and Eslamian, S., 2013, Power estimation and reliability evaluation of

municipal waste water and self-excited induction generator-based micro hydropower generation system,

Int. J. Hydrology Science and Technology, Vol. 3, No. 2, 176-191.

[79] Amiri, M. J., Abedi-Koupai, J., Eslamian, S., Mousavi, S. F. and Arshadi, M., 2013, Modelling Pb(II)

adsorption based on synthetic and industrial wastewaters by ostrich bone char using artificial neural

network and multivariate non-linear regression, Int. J. Hydrology Science and Technology, Vol. 3, No. 3,

221-240.

[80] Eslamian, S., Tarkesh Esfahany, S., Nasri, M. and Safamehr, M., 2013, Evaluating the potential of urban

reclaimed water in area of north Isfahan, Iran, for industrial reuses, Int. J. Hydrology Science and


[81] Ajigoh, E. and Eslamian, S., 2013, Nyando catchment GIS modeling of flood in undated areas, Journal of

Flood Engineering, Vol. 4, No. (1-2), 77–86.

[82] Galoie, M., Zenz, G. and Eslamian, S., 2013, Determining the high flood risk regions using a rainfall-

runoff modeling in a small basin in catchment area in Austria, Journal of Flood Engineering, Vol. 4, No.

(1-2), 9–27.

[83] Bazrkar, M. H., Fathian, F., and Eslamian, S., 2013, Runoff modeling in order to investigate the most

effective factors in flood events using system dynamic approach (Case study: Tehran Watershed, Iran),

Journal of Flood Engineering, Vol. 4, No. 1-2, 39–59.

[84] Galoie, M., Zenz, G. and Eslamian, S., 2013, Application of L-moments for IDF determination in an

Austrian basin, Int. J. Hydrology Science and Technology, Vol. 3, No. 1, 30-48.

[85] Rostamian, R., Eslamian, S. and Farzaneh, M. R., 2013, Application of standardised precipitation index for

predicting meteorological drought intensity in Beheshtabad watershed, central Iran, Int. J. Hydrology


[86] Bahmani, R., Radmanesh, F., Eslamian, S., Khorsandi, M. and Zamani, R., 2013, Proper Rainfall for Peak

Flow Estimation by Integration of L-Moment Method and a hydrologic model, International Research

Journal of Applied and Basic Sciences, Vol. 4 No. 10, 2959-2967.

[87] Mirabbasi, R., Anagnostou, E. N., Fakheri-Fard, A. Dinpashoh, Y. and Eslamian, S., 2013, Analysis of

meteorological drought in northwest Iran using the Joint Deficit Index, Journal of Hydrology, Vol. 492,

35–48.

[88] Gohari, A., Eslamian, S., Mirchi, A., Abedi-Koupaei, J., Massah-Bavani, A., Madani, K., 2013, Water

transfer as a solution to water shortage: A fix that can blackfire, Journal of Hydrology, Vol. 491, 23–39.

[89] Haghiabi, A. H., Mohammadzadeh-Habili, J., Eslamian, S. S., and S. F. Mousavi, 2013, Derivation of

Ewservior's Area-Capacity Equations Based on the Shape Factor, Iranian Journal of Science and

Technology, Vol. 37, No. C1, 163-167.

[90] Gohari, A., Eslamian, S., Abedi-Koupaei, J., Massah-Bavani, A., Wang, D., Madani, K., 2013, Climate

change impacts on crop production in Iran's Zayandeh-Rud River Basin. Science of The Total

Environment, Vol. 442, 405-419.

[91] Saatsaz, M., Azmin Sulaiman, W. N., Eslamian, S., Javadi, S., 2013, Development of a coupled flow and

solute transport modelling for Astaneh-Kouchesfahan groundwater resources, North of Iran, International

Journal of Water, Vol. 7, No.1/2, 80 – 103.

[92] Saatsaz, M., Azmin-Sulaiman, W. N., Eslamian, S., Mohammadi, K., 2013, Hydrogeochemistry and

groundwater quality assessment of Astaneh-Kouchesfahan Plain, Northern Iran, International Journal of

Water, Vol. 7, No. 1/2, 44 – 65.

[93] Eslamian, S., Amiri, M. J., Abedi-Koupai, J. and S. Shaeri-Karimi, 2013, Reclamation of unconventional

water using nano zero-valent iron particles: an application for groundwater, International Journal of Water,

Vol. 7, No. 1/2, 1-13.

[94] Amiri, M.J., Abedi-koupai, J., Eslamian, S. S., Mousavi, S. F., Hasheminejad, H., 2013, Modeling Pb (II)

adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system,

J Environ. Sci. Health A Tox. Hazard Subst. Environ. Eng., Vol. 48, No. 5: 543-58.

[95] Biabanaki, M., Tabatabaei Naeini, A. and S. S. Eslamian, 2012, Effects of Urbanization on Stream

Channels, Journal of Civil Engineering and Urbanism (JCEU), Vo. 2, No. 4, 136-142.

[96] Abdolhosseini, M., Eslamian, S., Mousavi, S. F., 2012, Effect of climate change on potential

evapotranspiration: a case study on Gharehsoo sub-basin, Iran, Vol. 2 No. 4, 362-372.

[97] Farzaneh, M. R., Eslamian, S. S., Samadi, Z. and A. Akbarpour, 2012, An appropriate general circulation

model (GCM) to investigate climate change impact, International Journal of Hydrology Science and





[98] Eslamian, S., Abedi-Koupai, J. and M. J. Zareian., 2012, Measurement and modelling of the water

requirement of some greenhouse crops with artificial neural networks and genetic algorithm, International

Journal of Hydrology Science and Technology, Vol. 2, No. 3, 237-251.

[99] Sadeghi, S. H., Mousavi, S. F., Eslamian, S. S., Ansari, S. and F. Alemi, 2012, A Unified Approach for

Computing Pressure Distribution in Multi-Outlet Irrigation Pipelines, Iranian Journal of Science and

Technology, Vol. 36, No. C2, 209-223.

[100] Alaghmand, S., Bin Abdullah, R., Abustan, I. and S. Eslamian, 2012, Comparison between capabilities of

HEC-RAS and MIKE11 hydraulic models in river flood risk modeling (a case study of Sungai Kayu Ara

River basin, Malaysia), International Journal of Environmental Science and Technology, Vol. 2, No. 3,

270-291.

[101] Galoie, M., Zenz, G., S. Eslamian and A. Motamedi., 2012, Numerical simulation of flood due to dam-

break flow using an implicit method, International Journal of Environmental Science and Technology,

Vol. 2, No. 2, 117-137.

[102] Ghazavi, R., A. B. Vali and S. Eslamian, 2012, Impact of Flood Spreading on Groundwater Level

Variation and Groundwater Quality in an Arid Environment, Water Resource Management, Vol. 26, No. 6,

1651-1663.

[103] Fakhri, M., Farzaneh, M. R., Eslamian, S. and M. J. Khordadi, 2012, Uncertainty Assessment of

Downscaled Rainfall: Impact of Climate Change on the Probability of Flood, Journal of Flood


[104] Gholami. A., Mahdavi, M. and S. Eslamian, 2012, Probability Distribution Choices for Minimum, Mean

and Maximum Discharges, by L-Moments in Mazandaran Province, IRAN, Journal of Flood Engineering,

Vol. 3, No. 1, 83-92.

[105] Shaeri karimi, S., Yasi, M. and S. S. Eslamian, 2012, Use of Hydrological Methods for Assessment of

Environmental Flow in a River Reach, International Journal of Environmental Science and Technology,

9(3), pp 549-558.

[106] Eslamian, S. S., Hassanzadeh, H., Abedi-Koupai, J. and M. Gheysari, 2012, Application of L-moments for

Regional Frequency Analysis of Monthly Drought Indices, Journal of Hydrologic Engineering, Vol. 17,

No. 1, 32-42.

[107] Farzaneh, M. R., Eslamian, S. S., Samadi, Z. and A. Akbarpour, 2012, An appropriate general circulation

model (GCM) to investigate climate change impact, International Journal of Hydrology Science and


[108] Eslamian, S. S., Khordadi, M. J. and J. Abedi-Koupai, 2011, Effects of Variations In Climatic Parameters

on Evapotranspiration In the Arid and Semi-Arid Regions, Global and Planetary Change, Vol. 78, 188–

194.

[109] Eslamian, S. S. and M. J. Amiri, 2011, Estimation of daily pan evaporation using adaptive neural-based

fuzzy inference system, International Journal of Hydrology Science and Technology, Vol. 1, Nos. 3/4,

164-175.

[110] Eslamian, S. S., Shaeri Karimi S. and F. Eslamian, 2011, A country case study comparison

on Groundwater and Surface Water Interaction, International Journal of Water, Vol. 6, Nos. 1/2, 117-136.

[111] Eslamian, S. S., Gohari, A., Zareian M. J. and A. Firoozfar, 2012, Estimating Penman-Monteith Reference

Evapotranspiration Using Artificial Neural Networks and Genetic Algorithm: A Case Study, The Arabian

Journal for Science and Engineering, Vol. 37, No. 4, 935-944.

[112] Hassanzadeh, H., Eslamian, S. S., Abedi-Koupai, J. and M. Gheysari, 2011, Application of L-moment for

evaluating drought indices of cumulative precipitation deficit (CPD) and maximum precipitation deficit

(MPD) based on regional frequency analysis, International Journal of Hydrology Science and Technology,

Vol. 1, Nos. 1/2, 88–104.

[113] Alipour, M. H., Shamsai, A., Eslamian, S. S. and R. Ghasemizadeh, 2011, A new fuzzy technique to find

the optimal solution in flood management, Journal of Flood Engineering, Vol. 2, No. 1, 1-9.

[114] Ghasemizade, M., Mohammadi K., and S. S. Eslamian, 2011, Estimation of design flood hydrograph for

an ungauged watershed, Journal of Flood Engineering, Vol. 2, No. 1/2, 27-36.

[115] Dhital, Y. P., Kayastha, R. B. and S. S. Eslamian, 2011, Precipitation and discharge pattern analysis: a

case study of Bagmati River basin, Nepal, Journal of Flood Engineering, Vol. 2, No. 1, 49-60.

[116] Saatsaz, M., Sulaiman, W.N.A. and S. S. Eslamian, 2011, GIS DRASTIC model for groundwater

vulnerability estimation of Astaneh-Kouchesfahan Plain, Northern Iran, International Journal of Water,

Vol. 6, No. 1/2, 1-14.

[117] Saatsaz, M., Chitsazan, M., Eslamian, S. S. and W.N.A. Sulaiman, 2011, The application of groundwater

modelling to simulate the behaviour of groundwater resources in the Ramhormooz Aquifer, Iran,

International Journal of Water, Vol. 6, Nos. 1/2, 29-42.




[118] Kambona, O. O., Stadel, C. and S. S. Eslamian, 2011, Perceptions of tourists on trial use and management

implications for Kakamega Forest, Western Kenya, Journal of Geography and Regional Planning Vol. 4,

No. 4, 243-250.

[119] Malekian, R., Abedi-Koupai, J., Eslamian, S. S., Mousavi, S. F., Abbaspour, K. C. and M. Afyuni, 2011,

Ion-exchange process for ammonium removal and release using natural Iranian zeolite, Applied Clay

Science, Vol. 51, 323–329.

[120] Malekian, R., Abedi-Koupai, J. and S. S. Eslamian, 2011, Influences of clinoptilolite and surfactant-

modified clinoptilolite zeolite on nitrate leaching and plant growth, Journal of Hazardous Materials, Vol.

185, 970–976.

[121] Malekian, R., Abedi-Koupai, J. and S. S. Eslamian, 2011, Use of Zeolite and Surfactant Modified Zeolite

as Ion Exchangers to Control Nitrate Leaching, World Academy of Science, Engineering and Technology,

Vol. 76, 657-661.

[122] Zaky, M. M. M., Salem, M. A. M., Persson, K. M. M. and S. S. Eslamian, 2011, Incidence of Aeromonas

species isolated from water and fish sources of Lake Manzala in Egypt, International Journal of Hydrology

Science and Technology, Vol. 1, Nos. 1/2, 47–62.

[123] Khorsandi, Z., Mahdavi, M., Salajeghe, A. and S. S. Eslamian, 2011, Neural Network Application for

Monthly Precipitation Data Reconstruction, Journal of Environmental Hydrology, Vol. 19, Paper 5, 1-12.

[124] Eslamian, S. S., 2010, The Physically-Statistically Based Region of Influence Approach for Flood

Regionalization, Journal of Flood Engineering, Vol. 1, No. 2, 149-158.

[125] Eslamian, S. S., 2010, Flood Regionalization Using a Modified Region of Influence Approach, Journal of

Flood Engineering, Vol. 1, No. 1, 51-66.

[126] Eslamian, S. S., Ghasemizadeh, M., Biabanaki, M. and M. Talebizadeh, 2010, A principal component

regression method for estimating low flow index, Water Resources Management, Vol. 24, No. 11, 2553-

2566.

[127] Ghazavi, R., Vali, A. B. and S. S. Eslamian, 2010, Impact of flood spreading on infiltration rate and soil

properties in an arid environment, Water Resources Management, Vol. 24, No. 11, 2781-2793.

[128] Rajabi, A., Sedghi, H., Eslamian, S. S. and H. Musavi, 2010, Comparison of Lars-WG and SDSM

downscaling models in Kermanshah (Iran), Ecol. Env. & Cons., Vol. 16, No. 4, 1-7.

[129] Rahnamai Zekavat, P., Ghasemizadeh, R., Eslamian, S. S. and S. Tarkesh Isfahani, 2010, Journal of Flood


[130] Chavoshi Borujeni, S., Sulaiman, W. N. A. and S. S. Eslamian, 2010, Regional Flood Frequency Analysis

Using L-Moments for North Karoon Basin Iran, Journal of Flood Engineering, Vol. 1, No. 1, 67-76.

[131] Kloub, N., Matouq, M., Krishan, M., Eslamian, S. S. and M. Abdelhadi, 2010, Monitoring of Water

Resources Degradation at Al-Azraq Oasis, Jordan Using Remote Sensing and GIS Techniques,

International Journal of Global Warming, Vol. 2, No. 1, 1-16.

[132] Akhavan S., Abedi-Koupai, J, Mousavi, S, F., Afyuni, M., Eslamian, S. S. and K. C. Abbaspour, 2010,

Application of SWAT model to investigate nitrate leaching in Hamadan–Bahar Watershed, Iran,

Agriculture, Ecosystems and Environment, Vol. 139, 675-688.

[133] Eslamian, S. S., Abedi-Koupai, J., Amiri, M, J., and A. R. Gohari, 2009, Estimation of Daily Reference

Evapotranspiration Using Support Vector Machines and Artificial Neural Networks in Greenhouse,

Research Journal of Environmental Sciences, Vol. 3, No. 4, 439-447.

[134] Eslamian, S. S. and N. Lavaei, 2009, Modelling Nitrate Pollution of Groundwater using Artificial Neural

Network and Genetic Algorithm in an Arid Zone, International Journal of Water, Special Issue on

Groundwater and Surface Water Interaction (GSWI), Vol. 5, No. 2, 194-203.

[135] Eslamian, S. S. and M. J. Khordadi, 2009, Comparing Rainfall and Discharge Trends in Karkhe Basin,

Iran, International Journal of Ecological Economics & Statistics (IJEES), Vol. 15, No. F09, 114-122.

[136] Eslamian, S. S. and B. Nekoueineghad, 2009, A Review on Interaction of Groundwater and Surface Water,

International Journal of Water, Special Issue on Groundwater and Surface Water Interaction (GSWI), Vol.

5, No. 2, 82-99.

[137] Eslamian, S. S. and N. Zamani, 2009, Innovations in Wind Modelling, International Journal of Global

Energy Issues, Special Issue on Wind Modelling and Frequency Analysis (WMFA), Vol. 32, No. 3, 175-

190.

[138] Eslamian, S. S. and H. Hasanzadeh, 2009, Detecting and Evaluating Climate Change Effect on Frequency

Analysis of Wind Speed in Iran, International Journal of Global Energy Issues, Special Issue on Wind

Modelling and Frequency Analysis (WMFA). Vol. 32, No. 3, 295 – 304.

http://www.springerlink.com/content/0920-4741/24/11/

http://www.springerlink.com/content/0920-4741/24/11/




[139] Eslamian, S. S., 2009, Editorial: Frontiers in Ecology and Environment, International Journal of Ecological

Economic & Statistics, Special Issue on Basin Ecology and Environment (BEE), Vol. 13, No. W09, 1-6.

[140] Eslamian, S. S. and M. Biabanaki, 2009, Low Flow Regionalization Models, International Journal of

Ecological Economic & Statistics, Special Issue on Stream Ecology and Low Flows (SELF), Vol. 12, No.

F08, 82-97.

[141] Eslamian, S. S., 2009, Editorial: An Ecologically Based Low Flow Review, International Journal of

Ecological Economic & Statistics, Special Issue on Stream Ecology and Low Flows (SELF), Vol. 12, No.

F08, 1-6.

[142] Nosrati, K., Eslamian, S. S., Shahbazi, A., Malekian, A. and M. M. Saravi, 2009, Application of Daily

Water Resources Assessment Model for Monitoring Water Resources Indices, International Journal of

Ecological Economic & Statistics, Special Issue on Basin Ecology and Environment (BEE), Vol. 13, No.

W09, 88-99.

[143] Abedi-Koupai, J., Amiri, M. J., and S. S. Eslamian, 2009, Comparison of Artificial Neural Network and

Physically Based Models for Estimating of Reference Evapotranspiration in Greenhouse, Australian

Journal of Basic and Applied Sciences, Vol. 3, No. 3, 2528-2535,

[144] Ebrahimizadeh, M. A., Amiri, M. J., Eslamian, S. S., Abedi-Koupai, J. and M. Khozaei, 2009, The Effects

of Different Water Qualities and Irrigation Methods on Soil Chemical Properties, Research Journal of

Environmental Sciences, Vol. 3, No. 4, 497-503.

[145] Matouq, M., Amarneh, I. A., Kloub, N., Badran, O., Al-Duheisat, S. A. and S. S. Eslamian, 2009,

Investigating the Effect of Combustion of Blending Jordanian Diesel Oil with Kerosene on Reducing the

Environmental Impacts by Diesel Engine, International Journal of Ecological Economic & Statistics,

Special Issue on Basin Ecology and Environment (BEE), Vol. 13, No. W09, 79-87.

[146] Eslamian S. S., Gohari, A., Biabanaki, M. and R. Malekian, 2008, Estimation of Monthly Pan Evaporation

Using Artificial Neural Networks and Support Vector Machines, Journal of Applied Sciences, Vol. 7, No.

19, 2900-2903.

[147] Abedi-Koupai J., Eslamian S. S. and J. Asad Kazemi, 2008, Enhancing the available Water Content in

Unsaturated Soil Zone using Hydrogel, to Improve Plant Growth Indices, Ecohydrology and

Hydrobiology, Vol. 8, No. 1, 3-11.

[148] Bazgeer, S., Kamali, G. A., Eslamian, S. S., Sedaghatkerdar, A. and I. Moradi, 2008, Pre-Harvest Wheat

Yield Prediction Using Agrometeorological Indices for Different Regions of Kordestan Province, Iran,

Research Journal of Environmental Sciences, Vol. 2, No. 4, 275-280.

[149] Eslamian, S. S. and H. Feizi, 2007, Maximum Monthly Rainfall Analysis Using L-moments for an Arid

Region in Isfahan Province, Iran, Journal of Applied Meteorology and Climatology, Vol. 46, No. 4, 494–

503.

[150] Modarres, R., Soltani, S. and S. S. Eslamian, 2007, The Use of Time Series Modeling for the

Determination of Rainfall Climates of Iran, International Journal of Climatology, Vol. 27, No. 6, 819–

829.

[151] Moradi, I., Nosrati, K. and S. S. Eslamian, 2007, Evaluation of the RadEst and ClimGen Stochastic

Weather Generators for Low-Medium Rainfall Regions, Journal of Applied Sciences, Vol. 7, No. 19,

2900-2903.

[152] Modarres R. and S. S. Eslamian, 2006, Streamflow Time Series Modeling of Zayandehrud River, Iranian

Journal of Science and Technology, Vol. 30, No. B4, 567-570.

[153] Mostafazadeh-fard, B., Osroosh, Y. and S. S. Eslamian, 2006, Development and Evaluation of an

Automatic Surge Flow Irrigation System, Journal of Agriculture and Social Sciences, Vol. 2, No. 3, 129-

132.

[154] Coles, N. A. and Eslamian, S., 2017, Definition of Drought, Ch. 1 in Handbook of Drought and Water

Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis

and Taylor, CRC Press, USA, 1-12.

[155] Dalezios, N. R., Dunkel, Z., Eslamian, S., 2017, Meteorological Drought Indices: Definitions, Ch. 3 in

Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by

Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 24-44.

[156] Goyal, M. K. Gupta, V., Eslamian, S., 2017, Hydrological Drought: Water Surface and Duration Curve

Indices, Ch. 4 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water

Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 45-72.

[157] Dalezios, N. R., Gobin, A., Tarquis Alfonso, A. M., and Eslamian, S., 2017, Agricultural Drought Indices:

Combining Crop, Climate, and Soil Factors, Ch. 5 in Handbook of Drought and Water Scarcity, Vol. 1:

Principles of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC

Press, USA, 73-90.

http://scialert.com/asci/author.php?author=S.%20Bazgeer

http://scialert.com/asci/author.php?author=Gh.A.%20Kamali

http://scialert.com/asci/author.php?author=S.S.%20Eslamian

http://scialert.com/asci/author.php?author=A.%20Sedaghatkerdar

http://scialert.com/asci/author.php?author=I.%20Moradi

http://www3.interscience.wiley.com/journal/114210475/issue




[158] TishehZan, P. and Eslamian, S., 2017, Agricultural Drought: Organizational Perspectives, Ch. 6 in



[159] Bazrkar, M. H., Eslamian, S., 2017, Ocean Oscillation and Drought Indices: Application, Ch. 8 in



[160] Basu, R., Singh, C. K., Eslamian, S., 2017, Cause and Occurrence of Drought, Ch. 9 in Handbook of

Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and

Eslamian F., Francis and Taylor, CRC Press, USA, 137-148.

[161] Bazrafshan, J., Hejabi, S., Eslamian, S., 2017, Drought Modeling Examples, Ch. 11 in Handbook of



[162] Jonathan Peter Cox, Sara Shaeri Karimi, Eslamian, S., 2017, Real-Time Drought Management, Ch. 13 in



[163] Garg, V. and Eslamian, S., 2017, Monitoring, Assessment, and Forecasting of Drought Using

Remote Sensing and the Geographical Information System. Ch. 14 in Handbook of Drought and Water

Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis

and Taylor, CRC Press, USA, 217-252.

[164] Dalezios, N. R., Tarquis Alfonso, A. M., and Eslamian, S., 2017, Drought Assessment and Risk Analysis,

Ch. 18 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed.

by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 323-344.

[165] Dalezios, N. R., Spyropoulosand, N. V., Eslamian, S., 2017, Remote Sensing in Drought Quantification

and Assessment, Ch. 21 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and

Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 377-396.

[166] Araghinejad, S., Hosseini-Moghari, S. M., Eslamian, S., 2017, Application of Data-Driven Models in

Drought Forecasting, Ch. 23 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought

and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 423-440.

[167] Vafakhah, M., and Eslamian, S., 2017, Application of Intelligent Technology in Rainfall Analysis, Ch. 24

in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by


[168] Vafakhah, M., Akbari Majdar, H. and Eslamian, S., 2017, Rainfall Prediction Using Time Series Analysis,



[169] González, M. H., Garbarini, E. M., Rolla, A. L., and Eslamian, S., 2017, Meteorological Drought Indices:

Rainfall Prediction in Argentina, Ch. 29 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of

Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA,

541-570.

[170] Hadizadeh, R. and Eslamian, S., 2017, Modeling Hydrological Process by ARIMA–GARCH Time Series,



[171] Mujere, N., Yang, X. and Eslamian, S., 2017, Gradation of Drought-Prone Area, Ch. 31 in Handbook of



[172] Mahmudul Haque, M., Amir Ahmed, A., Rahman, A., Eslamian, S., 2017, Drought Losses to Local

Economy, Ch. 33 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water

Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 627-642.

[173] Fakhruddin, B. S. H. M., Eslamian, S., 2017, Analysis of Drought Factors Affecting the Economy, Ch. 34

in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by


[174] Dalezios, N. R., Eslamian, S., 2017, Environmental Impacts of Drought on Desertification Classification,

Ch. 3 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of


45-64.

[175] Nazif, S. and Tavakolifar, H., Eslamian, S., 2017, Climate Change Impact on Urban Water Deficit, Ch. 5

in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and





[176] Shahid, S., Alamgir, M., Wang, X.-J., Eslamian, S., 2017, Climate Change Impacts on and Adaptation to

Groundwater, Ch. 6 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and

Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC

Press, USA, 107-124.

[177] Orimoogunje, O. O. I., Eslamian, S., 2017, Minimizing the Impacts of Drought, Ch. 8 in Handbook of

Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and Water Scarcity,

Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 143-162.

[178] Maleksaeidi, H., Keshavarz, M., Karami, E., Eslamian, S., 2017, Climate Change and Drought: Building

Resilience for an Unpredictable Future, Ch. 9 in Handbook of Drought and Water Scarcity, Vol. 2:

Environmental Impacts and Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F.,

Francis and Taylor, CRC Press, USA, 163-186.

[179] Reyhani, M. N., Eslamian, S., Davari, A., 2017, Sustainable Agriculture: Building Social-Ecological

Resilience, Ch. 10 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and


Press, USA, 187 -204.

[180] Crusberg, T. C., Eslamian, S., 2017, Drought and Water Quality, Ch. 11 in Handbook of Drought and

Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and Water Scarcity, Ed. by


[181] Gaaloul, N., Eslamian, S., and Laignel, B., 2017, Contamination of Groundwater in Arid and Semiarid

Lands, Ch. 16 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis

of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA,

291-314.

[182] Banjoko, B., Eslamian, S., 2017, Sanitation in Drought, Ch. 17 in Handbook of Drought and Water

Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and Water Scarcity, Ed. by Eslamian S.

and Eslamian F., Francis and Taylor, CRC Press, USA, 315-330.

[183] Davari, A., Bagheri, A., Reyhani, M. N., Eslamian, S., 2017, Environmental Flows Assessment in Scarce

Water Resources, Ch. 18 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and



[184] Qian, Q., Eslamian, S., 2017, Streamflow Quality in Low-Flow Conditions, Ch. 20 in Handbook of



[185] Mohammadzade Miyab, N., Eslamian, S., Dalezios, N. R., 2017, River Sediment in Low Flow Condition,

Ch. 21 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of


387-408.

[186] Pérez-Blanco, C. D., Delacámara., G., Gómez., C. M., Eslamian, S., 2017, Crop Insurance in Drought

Conditions, Ch. 23 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and



[187] Kahrizi, D., Esfahani, K., Ashraf Mehrabi, A., Ghaheri, M., Azizi Aram, Z., Khosravi, S., Eslamian, S.,

2017, Biotechnology for Drought Improvement, Ch. 24 in Handbook of Drought and Water Scarcity, Vol.

2: Environmental Impacts and Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian

F., Francis and Taylor, CRC Press, USA, 445-460.

[188] Wade, P., Eslamian, S., 2017, Water Issues from a System Dynamics Perspective, Ch. 25 in Handbook of



[189] Rahman, A., Hajani, E., Eslamian, S., 2017, Rainwater Harvesting in Arid Regions of Australia, Ch. 26 in

Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and


[190] Dayani, S., Sabzalian, M. R., Hadipour, M. Eslamian, S., 2017, Water Scarcity and Sustainable Urban

Green Landscape, Ch. 30 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and



[191] Gohari, A., Zareian, M. J., Eslamian, S., Nazari, R. 2017, Interbasin Transfers of Water: Zayandeh-Rud

River Basin, Ch. 32 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and






[192] Banjoko, B., Eslamian, S., 2017, Environmental Evaluation: Lessons Learned from Case Studies, Ch. 33 in

Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and


Citation: Dr. Kaveh Ostad-Ali-Askari et.al. (2017) A Study on the Effects of Earth Surface and Metrological

Parameters on River Discharge Modeling Using SWAT Model, Case Study: Kasillian Basin, Mazandaran

Province, Iran, International Journal of Constructive Research in Civil Engineering, 3(4), pp.99-120. DOI:

http://dx.doi. org/10.20431/2454-8693.0304010

Copyright: © 2017 Dr. Kaveh Ostad-Ali-Askari, This is an open-access article distributed under the terms

of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction

in any medium, provided the original author and source are credited.

http://dx.doi/

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A Study on the Effects of Earth Surface and Metrological ... · temperature, solar radiation, wind speed and relative humidity. This software requires at least temperature and precipitation